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How much goodness do we expect from our neighbours? People living nearby might be locked with each other in interdependencies by spatial proximity but often do not know each other more personally. This chapter explores the question of how latent neighbourliness – an expectation that neighbours will have our back even though we might not know them – emerges. We draw on statistical analyses of survey data from four neighbourhoods in Berlin, Germany, and a pre- and post-COVID-19 methodology, therefore capturing a time when people were asked to stay home and within their neighbourhoods. Our findings demonstrate that latent neighbourliness is neither significantly associated with personal support from neighbours when facing important challenges, nor personal support experienced in the neighbourhood with others whom we know, but who are not neighbours (e.g., family members). Neither is latent neighbourliness a fixed attitude that can be explained by individual characteristics and/or positions (such as age, gender, education, income, one’s employment situation and others). In contrast, we find that, apart from individual generalised trust towards others, the neighbourhood setting itself shapes levels of latent neighbourliness among all demographics. Additionally, those with younger children show higher latent neighbourliness, most likely a result of moral geographies. We argue that caring for children in public and experiencing or displaying moral codes that others can read makes it easier to develop an expectation of goodwill (or for that matter, hostility) from neighbours, without having more durable ties to them.

In an urbanising world, neighbouring is perceived to be steadily losing significance and a remnant of the past. The same belief can also be found in China where rapid urbanisation has had a tremendous impact on the social networks and neighbourhood life of urban residents. This chapter challenges the common perception of neighbouring in demise and argues that neighbouring remains an important form of social relationship, even if the meanings and role of neighbouring have changed. This chapter first charts the changing role of neighbouring from the socialist era to post-reform China. It then provides an account of four common types of neighbourhoods in Chinese cities – work-unit estates, traditional courtyards, commodity housing estates and urban villages – and considers how and why neighbouring in different ways still matters to them. In pre-reform socialist China, neighbourhood life and neighbouring comprised much of the daily social life of residents. Since the reform era, with the proliferation of private commodity housing estates, middle-class residents prioritise comfort, security and privacy, such that neighbouring levels have subsided. Nevertheless, in other neighbourhood types, such as work-unit housing estates, traditional courtyards and urban villages, neighbours still rely upon one another for various reasons.

Singapore, a multiracial nation, where 80% of the population resides in mid- to high-rise public housing estates, provides an interesting milieu to study neighbouring in the context of high-density living. Its geopolitical position and socio-demographic features – such as an ageing demographic, changing family structure, and increasingly diverse population – have rendered social cohesion an integral aspect of national and neighbourhood-level policies, programmes, and institutions. However, these programmes and policies are built on a relatively static national narrative around mutuality, harmony, and community bonding that rarely takes into consideration current social, temporal, and spatial constructions of neighbouring. It is in this light that we re-examine the social construction of ‘neighbours’ and ‘neighbourliness’ in Singapore using a holistic quality of life (QoL) framework to better understand both institutional and lived forms of neighbourliness. A mixed-methods research approach, comprising 243 semi-structured interviews (161 with a Q method component) and a large-scale survey comprising 3,134 participants, was conducted to explore and assess current norms, attitudes, and practices around neighbouring. Our findings show a dominant set of practices around neighbouring that are polite, minimal, and often referred to as ‘hi/bye’. we note two particular perspectives of the relative importance of neighbours in contributing to QoL and consider the effects of hi/bye neighbouring upon an older group of residents for whom neighbours are still important. This deeper understanding around neighbouring in public housing estates also brings relevant insights to related social cohesion policies and programmes.

This chapter is based on 68 interviews in 12 mobile home parks that were part of a larger ethnographic study, conducted in 2005–2010 in West-Central Florida. Data analysis revealed diverse patterns of perception, sentiment, and interaction among neighbours, here understood as ‘neighbour culture’, both across and within communities. American mobile home communities are characterised by a high propinquity of residents and exposure to cultural housing stigma; however, these conditions alone did not determine local neighbour culture. In the analysis, I illustrate prominent patterns of neighbouring, ranging from perceptions and treatments of neighbours as (imagined) ‘family’ in senior communities to, partially, ‘trailer trash’ in suburban family communities. Going beyond description, I argue that the identified supportive, minimalist, and antagonistic forms of neighbour culture are linked to broader spatial and social structural contexts, as well as personal identities. This chapter’s findings have the potential to strengthen the theoretical framing and research of neighbouring in local and global perspectives in the future.

The purpose of this paper is to describe and position the state-of-the-art of text and image mining methods in business research. By providing a detailed conceptual and technical review of both methods, it aims to increase their utilization in service research.

On a first stage, the authors review business literature in marketing, operations and management concerning the use of text and image mining methods. On a second stage, the authors identify and analyze empirical papers that used text and image mining methods in services journals and premier business. Finally, avenues for further research in services are provided.

The manuscript identifies seven text mining methods and describes their approaches, processes, techniques and algorithms, involved in their implementation. Four of these methods are positioned similarly for image mining. There are 39 papers using text mining in service research, with a focus on measuring consumer sentiment, experiences, and service quality. Due to the nonexistent use of image mining service journals, the authors review their application in marketing and management, and suggest ideas for further research in services.

This manuscript focuses on the different methods and their implementation in service research, but it does not offer a complete review of business literature using text and image mining methods.

The results have a number of implications for the discipline that are presented and discussed. The authors provide research directions using text and image mining methods in service priority areas such as artificial intelligence, frontline employees, transformative consumer research and customer experience.

The manuscript provides an introduction to text and image mining methods to service researchers and practitioners interested in the analysis of unstructured data. This paper provides several suggestions concerning the use of new sources of data (e.g. customer reviews, social media images, employee reviews and emails), measurement of new constructs (beyond sentiment and valence) and the use of more recent methods (e.g. deep learning).

The purpose of this paper is to present a technical perspective when implementing the Slovenian open access infrastructure that consists of four institutional repositories (IRs) and a national portal (NP) that aggregates content from the repositories in order to provide a common search engine, recommendations of similar documents, and similar text detection.

During the project, the necessary legal background and processes for mandatory submissions of final study works, research publications and research data were established, as well as processes for data exchange between the IRs and the NP, and processes for similar text detection.

The consortium consisted of four Slovenian universities that significantly differ in size, organisation, and workflows. It was anticipated that exactly the same legal background and software would be used for the four repositories. It turned out that complete unification was impossible due to the differences.

The national open access infrastructure will improve the visibility of Slovenian research organisations. It supports the compliance with the funders’ open access mandates. The established infrastructure enables the depositing and archiving of approximately 80 percent of the peer-reviewed scientific publications that are annually published by Slovenian researchers. At the same time, the majority of final study works from Slovenian higher education institutions are available in full-text format.

This paper describes a technical perspective for setting up a national open access infrastructure, which has not been described in the literature previously.

Multi-criteria recommender systems (MC-RSs) allow users to express their preference in multiple aspects. Bayesian flexible mixture model (BFMM) is a model-based RS which extends FMM from single-criterion to MC. However, results of BFMM have a preference on different rating pattern problem. In single-criterion, FMM with decoupled normalization and W’s transposed function try to solve this problem. However, these techniques are applied to each criterion separately. Then, the relationship among criteria will be lost. This paper aims to solve different rating pattern problems and loss of the relationship between criteria.

The proposed method is combining between BFMM and rating conversion. First, mean and variance normalization is applied to make MC ratings of an active user and a neighbor lying on the same plane. After that, a pattern of each user is extracted using principal component analysis (PCA). Next, the pattern is used to convert neighbors’ MC ratings to the active user aspect. After that, converted MC ratings of neighbors are aggregated to be overall ratings using multiple linear regression (MLR). Finally, overall rating of the active user toward the target item is predicted using weighted average on the derived neighbors’ overall ratings where the similarity from BFMM acts as a weight.

The experimental results show that the proposed method where all criteria ratings are converted simultaneously can improve the performance of recommendation.

The proposed method predicts overall rating of the active user by converting MC ratings of each neighbor to the active user aspect at the same time, which can reduce the loss of the relationship between criteria.